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AI-GEOSTATS: sample distances bigger than range

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  • Lorenz Dobler
    hell list, i did a little exercise with ordinary kriging with extreme sample distances. as i understand: if all distances between samples and gridpoint are
    Message 1 of 2 , Dec 17, 2003
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      hell list,

      i did a little exercise with ordinary kriging with extreme sample distances.
      as i understand: if all distances between samples and gridpoint are bigger
      than the range, the kriging estimation should be equal to the sample mean =>
      all samples should get the same weight.

      in my example everything works well as long as the sample-grid-point
      distances are smaller than the range. when i "put" all sample-points very
      far away from the gridpoint (without changing the spatial pattern!) then the
      values of the right hand side (d-kovariance-matrix/vector) become zero
      except for the lagrange parameter (=1), that's ok, but why do the weights
      all get individual values and the estimated value is not equal the
      arithmetic mean ?? is there any mistake i don't recognized

      hope someone can help



      Lorenz Dobler

      Universität Münster
      Institut für Geoinformatik
      Robert-Koch-Strasse 26-28
      D-48149 Münster

      Tel.: 0251/83-30089
      Fax: 0251/83-39763
      email: doblerl@...


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    • Isobel Clark
      Lorenz Your samples also have to be out of the range of influence of one another to get equal weights. The left hand side of the equations needs to be an
      Message 2 of 2 , Dec 17, 2003
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        Lorenz

        Your samples also have to be out of the range of
        influence of one another to get equal weights. The
        left hand side of the equations needs to be an
        identity matrix (well, with the total sill down the
        diagonal and zeroes everywhere else) to get equal
        weights.

        Isobel {Clark}
        http://geoecosse.bizland.com/whatsnew.htm

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